Inferred identity

ABSTRACT

Techniques for inferring the identity (e.g., member profile attributes) of members of an online social network service are described. According to various embodiments, a member profile attribute missing from a member profile page associated with a particular member of an online social network service is identified. Member profile data and behavioral log data associated with a plurality of members of the online social network service is then accessed. Thereafter, a prediction modeling process is performed, based on a prediction model and feature data including the member profile data and the behavioral log data, to generate a confidence score associated with the particular member and the missing member profile attribute, the confidence score indicating a likelihood that the missing member profile attribute corresponds to a candidate value.

TECHNICAL FIELD

The present application relates generally to data processing systemsand, in one specific example, to techniques for inferring the identity(e.g., member profile attributes) of members of an online social networkservice.

BACKGROUND

Online social network services such as LinkedIn) offer a variety ofproducts and services to millions of members. Typically, each member ofthe online social network service may maintain a member profile pagethat includes various information (or member profile attributes)associated with the member, such as a member photo, employmentinformation, educational information, title, skills, geographiclocation, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 is a block diagram showing the functional components of a socialnetworking service, consistent with some embodiments of the invention;

FIG. 2 is a block diagram of an example system, according to variousembodiments;

FIG. 3 is a flowchart illustrating an example method, according tovarious embodiments;

FIG. 4 is a flowchart illustrating an example method, according tovarious embodiments;

FIG. 5 is a flowchart illustrating an example method, according tovarious embodiments;

FIG. 6 is a flowchart illustrating an example method, according tovarious embodiments;

FIG. 7 is a flowchart illustrating an example method, according tovarious embodiments;

FIG. 8 illustrates an example mobile device, according to variousembodiments; and

FIG. 9 is a diagrammatic representation of a machine in the example formof a computer system within which a set of instructions, for causing themachine to perform any one or more of the methodologies discussedherein, may be executed.

DETAILED DESCRIPTION

Example methods and systems for inferring the identity (e.g., memberprofile attributes) of members of an online social network service aredescribed. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of example embodiments. It will be evident, however, toone skilled in the art that the present invention may be practicedwithout these specific details.

According to various example embodiments, an identity inference systemis configured to infer missing member profile attributes associated witha member of an online social network service. For example, if a memberhas a missing employer (e.g., company) attribute, educational (e.g.,school) attribute, geographic location attribute, title attribute,skills attribute, etc., the system is configured to analyze the existingdata (e.g., member profile data and behavioral log data) associated withthat member, as well as existing data of other members of the onlinesocial network, in order to infer a value for the missing member profileattribute.

For example, in some embodiments, the system may access member profiledata and behavioral log data associated with members of the onlinesocial network service, the behavioral log data indicating interactionsby the members with one or more products of the online social networkservice. As described in more detail below, the system may then inputone or more features extracted from the member profile data and/orbehavioral log data into a computer-based prediction model, such as alogistic regression model, and perform a prediction modeling processbased on the feature data to generate a confidence score associated witha missing member profile attribute of a particular member, theconfidence score indicating a likelihood that the missing member profileattribute corresponds to a candidate value. If the confidence score isgreater than a predetermined value, then the system may determine thatthe missing member profile attribute likely corresponds to thatcandidate value

Non-limiting examples of member profile attributes that may be inferredinclude name, title, industry, geographic location, country, region,contact information, e-mail address, gender, current employer, previousemployer, current educational institution, previous educationalinstitution, degree, field of study, skills, recommendations,endorsements, company size, seniority level, and so on. Non-limitingexamples of behavioral log data include any information indicating how amember interacts with online content (e.g., an online social networkservice website and any webpages and products associated therewith). Forexample, in the case of the online social network service LinkedIn®, thebehavioral log data of members includes information indicating when themember logged into the site, how long the member remained on the site,what was displayed to them, what they viewed, impressions, what theyclicked on, social activity information (e.g., what they liked, shared,followed, posted, commented on, etc.), products that they interactedwith (e.g., a content feed or network update stream (NUS), memberprofile pages of other members, a people you may know (PYMK) product, aJobs You May Be Interested In (JYMBII) product, a company page, a grouppage, an influencer page, a University/school page, etc.), geolocationinformation (received from a mobile device of the member), computingplatform information (e.g., whether the member accessed the site viadesktop or mobile device), IP address information (e.g., the IPaddresses associated with login requests to login to the website thatwere received from the member), member connections of the member,invitations sent by the member, address book or contact informationuploads by the member to the site, and so on.

The system may take into account any combination of feature datadescribed above (e.g., the aforementioned member profile data andbehavioral log data) in order to determine a probability or “confidencescore” indicating the likelihood that a missing member profileattributes of the particular member corresponds to a candidate value. Asdescribed above, the system may perform computer-based predictionmodeling, using one or more computer-based prediction models (e.g.,statistical machine learning models), in order to generate theaforementioned confidence score. Examples of prediction models include alogistic regression model, a gradient-boosted machine (GBM) model, aNaïve Bayes model, a support vector machines (SVM) model, a decisiontrees model, and a neural network model.

In some embodiments, a prediction model may be trained based on featuredata (e.g., member profile data, behavioral log data, etc.) associatedwith members having a known candidate value for a member profileattribute (e.g., members listing a particular school such as Stanford,or a particular employer such as LinkedIn®, or a particular geographiclocation such as San Francisco Bay Area, or a particular title such ascomputer engineer, or a particular skill such as HTML, etc.). During thetraining process, statistical trends and patterns associated with thesemembers having the known candidate value for the member profileattribute may be identified, and coefficients of the model may betrained and refined accordingly, such that the trained model reflectsthe relative weight, importance, or relevance of various features forthe purposes of determining whether a specific member is also associatedwith that candidate value for that member profile attribute (e.g.,whether a specific member went to Stanford). For example, thecoefficients of the trained model may reflect that the attributes ofemail address domain and IP address have a high correlation to themember profile attribute of location, although they have a lowercorrelation to the member profile attribute of title, and so thecoefficients in the model associated with the attributes of emailaddress domain and IP address may be weight accordingly. Once trained,the prediction model may receive available features associated with aspecific member (e.g., member profile data, social activity information,geolocation information, IP address login information, etc.) in order todetermine the likelihood that a member profile attribute of this membercorresponds to a given candidate value.

Since each member profile attribute may be associated with multiplecandidate values (e.g., a company attribute could correspond tocandidate values such as LinkedIn®, Google®, Apple®, etc.), the systemmay generate multiple confidence scores associated with multiplecandidate values, each confidence score indicating the likelihood thatthat candidate value equates to the missing member profile attribute ofa member. In some example embodiments, the system may rank the candidatevalues based on the confidence scores in order to determine that themember likely works at the highest ranked candidate value. Instead or inaddition, the system may determine whether one or more of the candidatevalues are greater than a predetermined threshold, as described above.In some example embodiments, a specific prediction model may be trainedfor each type of member profile attribute (e.g., a trained model for theattribute of company, a trained model for the attribute of university,etc.). Moreover, in some embodiments, a specific prediction model may betrained for each specific candidate value associated with a memberprofile attribute (e.g., a trained model for the candidate value ofLinkedIn® for the company attribute, a trained model for the candidatevalue of Google® for the company attribute, etc.).

An example of a specific feature that may be included in the featuredata includes member connection information identifying memberconnections of each member of a social network. For example, wheninferring whether John Smith works at LinkedIn®, the system maydetermine that if a large number of the member connections of John Smithwork at LinkedIn®, it is more likely that John Smith also works atLinkedIn®. For example, a prediction model may be trained on memberprofile data and behavioral log data of members that work at LinkedIn®and determine that such members tend to have a large number of memberconnections that work at LinkedIn®, and perhaps a relatively smallernumber of member connections that work at other companies. Similarly,the prediction model may be trained on member profile data andbehavioral log data of members that work at Google@ and determine that,for example, these members tend to have a small number of memberconnections that work at LinkedIn®, and perhaps a relatively largernumber of member connections that work at Google®, and so on.Accordingly, when training the prediction model on data of membersassociated with a known candidate value (e.g., members that work atLinkedIn®), the system may include, in a feature vector associated witheach particular member, a numerical value indicating the number ofmember connections of that particular member that share the same companyas the candidate company (e.g., the number of member connections thatwork at LinkedIn®). Likewise, when later utilizing the prediction modelto generate a confidence score associated with John Smith having aparticular candidate value (e.g., LinkedIn®), the system may include, ina feature vector of feature data associated with John Smith, a numericalvalue indicating the number of member connections of John Smith thatshare the same company as the candidate company (e.g., the number ofmember connections that work at LinkedIn®). In this non-limitingexample, the greater this numerical value, the greater the confidencescore generated by the prediction model. Likewise, member connectioninformation may be utilized in a similar manner when inferring otherattributes, such as location, school, title, skills, etc. For example,when inferring location attributes, the system may utilize a featureindicating how many member connections of a given member share the samecountry/region as the candidate country/region.

Another example of a specific feature that may be included in thefeature data includes IP address information identifying IP addressesassociated with login requests by the members of the online socialnetwork. For example, the system may infer that members who log in fromthe same/similar IP address tend to work at the same company.Accordingly, the system may include, in a feature vector of feature dataassociated with a given member, a numerical value indicating an IPaddress used by that member to login to a website, or a numerical valueindicating how members having the same/similar IP address as the givenmember share the same company as the candidate company. Likewise, IPaddress information may be utilized in a similar manner when inferringother attributes, such as location, school, title, skills, etc. Forexample, when inferring location attributes, the system may utilize afeature indicating how many members associated with the same IP addressas the given member share the same country/region as the candidatecountry/region.

Another example of a specific feature that may be included in thefeature data includes profile view information identifying profile viewsby members of the online social network. For example, the system maydetermine that if member profile X is frequently viewed by the samemembers that view member profile Y (such that Y is termed a “co-viewedprofile” of X and vice versa), then the member X and member Y likelywork together. Accordingly, the system may include, in a feature vectorof feature data associated with a given member, a numerical valueindicating how many co-viewed profiles of that member share the samecompany as the candidate company (e.g., people that view this memberfrequently view a certain number of other members working at LinkedIn®).In this non-limiting example, the greater this numerical value, thegreater the confidence score generated by the prediction model.Likewise, profile view information may be utilized in a similar mannerwhen inferring other attributes, such as location, school, title,skills, etc. For example, when inferring location attributes, the systemmay utilize a feature indicating how many co-viewed profiles of thatmember share the same country/region as the candidate country/region.

Another example of a specific feature that may be included in thefeature data includes email domain information identifying email domainsassociated with the members of the online social network. For example,the system may infer that members who sign up for a social networkaccount with the same email domain (e.g., LinkedIn®.com) tend to work atthe same company (e.g., LinkedIn®). Accordingly, the system may include,in a feature vector of feature data associated with a given member, anumerical value indicating an email domain used by that member to signup for an account on a website, or a numerical value indicating howmembers associated with the same email domain as the member share thesame company as the candidate company. Likewise, email domaininformation may be utilized in a similar manner when inferring otherattributes, such as location, school, title, skills, etc.

Another example of a specific feature that may be included in thefeature data includes invitation information identifying invitationstransmitted by the members of the online social network. For example,when inferring whether John Smith works at LinkedIn®, the system maydetermine that if John Smith has transmitted a large number ofinvitations to other members that work at LinkedIn, it is more likelythat John Smith also works at LinkedIn®. Accordingly, the system mayinclude, in a feature vector of feature data associated with a givenmember, a numerical value indicating the number of member invitationssent by that particular member to others that share the same company asthe candidate company (e.g., members that work at LinkedIn). Likewise,invitation information may be utilized in a similar manner wheninferring other attributes, such as location, school, title, skills,etc.

Another example of a specific feature that may be included in thefeature data includes address book information identifying records inaddress books associated with members of the online social network. Forexample, the system may infer that, if a member uploads an address bookthat includes a large number of users that work at LinkedIn®, it is morelikely that the member also works at LinkedIn®. Accordingly, the systemmay include, in a feature vector of feature data associated with a givenmember, a numerical value indicating the number of users in an addressbook uploaded by that particular member that share the same company asthe candidate company (e.g., users that work at LinkedIn®). As anotherexample, the system may infer that, if a particular member frequentlyco-occurs in address books with others that work at LinkedIn®, then thatparticular member likely also works at LinkedIn®. Accordingly, thesystem may include, in a feature vector of feature data associated witha given member, a numerical value indicating how many times that memberhas co-occurred (in one or more address books) with at least one userthat shares the same company as the candidate company, or how many timesthey have co-occurred with a specific user works at the candidate valuecompany, and so on. As another example, the system may count how manytimes a member appears in address books of members who work at thecandidate company. For example, the system may determine that if someoneappears very often in address books of LinkedIn® employees, he/she ismore likely to have worked at LinkedIn®. Accordingly, the system mayinclude, in a feature vector of feature data associated with a givenmember, a numerical value indicating how many times that member hasappeared in address books of users who work at the candidate company.Likewise, address book information may be utilized in a similar mannerwhen inferring other attributes, such as location, school, title,skills, etc.

Another example of a specific feature that may be included in thefeature data includes alumni group membership information, associatedwith the members of the online social network. For example, the systemmay infer that members who join the same alumni group (e.g., StanfordAlumni) tend to graduate from the same school (e.g., Stanford).Accordingly, the system may include, in a feature vector of feature dataassociated with a given member, a numerical value indicating the numberof members that attended a candidate school that have joined a groupthat the given member has joined. Likewise, group membership informationmay be utilized in a similar manner when inferring other attributes,such as location, company, title, skills, etc.

Another example of a specific feature that may be included in thefeature data includes geographic location information (e.g., city,state, country, region, etc.) of the members of the online socialnetwork. For example, the system may infer that if a member hasspecified that they are in a given country or region (e.g., when theysigned up for an account on the online social network service), thenthey likely attend a school in that country or region, and they likelydo not attend a school in some other country or region. Accordingly, thesystem may include, in a feature vector of feature data associated witha given member, a numerical value indicating a country or region (e.g.,as specified by the member when they signed up for an account on theonline social network service). Likewise, geographic locationinformation may be utilized in a similar manner when inferring otherattributes, such as location, company, title, skills, etc.

Another example of a specific feature that may be included in thefeature data includes gender distribution information of the members ofthe online social network. For example, if the system determines thatthe member is male, it is less likely that they attend a female-onlyschool (or a school where most members are female). Accordingly, thesystem may include, in a feature vector of feature data associated witha given member, a numerical value indicating the number of a number orproportion of members having the same gender as the given member at thecandidate school. Likewise, gender distribution information may beutilized in a similar manner when inferring other attributes, such aslocation, company, title, skills, etc.

Another example of a specific feature that may be included in thefeature data includes industry information associated with the membersof the online social network. For example, the system may infer thatmembers who have the same industry attribute (e.g., Music Industry) tendto graduate from same or similar schools. Accordingly, the system mayinclude, in a feature vector of feature data associated with a givenmember, a numerical value indicating the number of members having thesame industry attribute as the given member that attended the candidateschool. Likewise, industry information may be utilized in a similarmanner when inferring other attributes, such as location, company,title, skills, etc.

In some embodiments, the system may infer title in conjunction withinferring company. For example, title and company are often relatedattributes that are specified by the member in conjunction with eachother. In some embodiments, the system may infer the title of a givenmember by first inferring a company of the given member (e.g., using anyembodiments described above), and then identifying a set of one or morefirst degree member connections of the given member who have worked atthe inferred company. The system may then identify a first one of thatset of members that is “most similar” to the given member, by comparingthe skills of that first member with the skills (either known orinferred) of the given member. The system may then infer that the titleof the given member corresponds to the title of the first member.

Various examples above describe features and/or numerical values beinginserted into feature vectors, and such features and/or numerical valuesmay represent a count over a particular period of time (e.g., aparticular week, a group of weeks, a particular month, a group ofmonths, etc.). For example, when inferring location attributes, thesystem may utilize a feature indicating how many members associated withthe same/similar IP address as the given member share the samecountry/region as the candidate country/region. Thus, this feature mayinclude a count of how many members have logged into a website based onthat same/similar IP address during a particular week, a group of weeks,a particular month, a group of months, etc.

FIG. 1 is a block diagram illustrating various components or functionalmodules of a social network service such as the social network system20, consistent with some embodiments. As shown in FIG. 1, the front endconsists of a user interface module (e.g., a web server) 22, whichreceives requests from various client-computing devices, andcommunicates appropriate responses to the requesting client devices. Forexample, the user interface module(s) 22 may receive requests in theform of Hypertext Transport Protocol (HTTP) requests, or otherweb-based, application programming interface (API) requests. Theapplication logic layer includes various application server modules 14,which, in conjunction with the user interface module(s) 22, generatesvarious user interfaces (e.g., web pages) with data retrieved fromvarious data sources in the data layer. With some embodiments,individual application server modules 24 are used to implement thefunctionality associated with various services and features of thesocial network service. For instance, the ability of an organization toestablish a presence in the social graph of the social network service,including the ability to establish a customized web page on behalf of anorganization, and to publish messages or status updates on behalf of anorganization, may be services implemented in independent applicationserver modules 24. Similarly, a variety of other applications orservices that are made available to members of the social networkservice will be embodied in their own application server modules 24.

As shown in FIG. 1, the data layer includes several databases, such as adatabase 28 for storing profile data, including both member profile dataas well as profile data for various organizations. Consistent with someembodiments, when a person initially registers to become a member of thesocial network service, the person will be prompted to provide somepersonal information, such as his or her name, age (e.g., birthdate),gender, interests, contact information, hometown, address, the names ofthe member's spouse and/or family members, educational background (e.g.,schools, majors, matriculation and/or graduation dates, etc.),employment history, skills, professional organizations, and so on. Thisinformation is stored, for example, in the database with referencenumber 28. Similarly, when a representative of an organization initiallyregisters the organization with the social network service, therepresentative may be prompted to provide certain information about theorganization. This information may be stored, for example, in thedatabase with reference number 28, or another database (not shown). Withsome embodiments, the profile data may be processed (e.g., in thebackground or offline) to generate various derived profile data. Forexample, if a member has provided information about various job titlesthe member has held with the same company or different companies, andfor how long, this information can be used to infer or derive a memberprofile attribute indicating the member's overall seniority level, orseniority level within a particular company. With some embodiments,importing or otherwise accessing data from one or more externally hosteddata sources may enhance profile data for both members andorganizations. For instance, with companies in particular, financialdata may be imported from one or more external data sources, and madepart of a company's profile.

Once registered, a member may invite other members, or be invited byother members, to connect via the social network service. A “connection”may require a bi-lateral agreement by the members, such that bothmembers acknowledge the establishment of the connection. Similarly, withsome embodiments, a member may elect to “follow” another member. Incontrast to establishing a connection, the concept of “following”another member typically is a unilateral operation, and at least withsome embodiments, does not require acknowledgement or approval by themember that is being followed. When one member follows another, themember who is following may receive status updates or other messagespublished by the member being followed, or relating to variousactivities undertaken by the member being followed. Similarly, when amember follows an organization, the member becomes eligible to receivemessages or status updates published on behalf of the organization. Forinstance, messages or status updates published on behalf of anorganization that a member is following will appear in the member'spersonalized data feed or content stream. In any case, the variousassociations and relationships that the members establish with othermembers, or with other entities and objects, are stored and maintainedwithin the social graph, shown in FIG. 1 with reference number 30.

The social network service may provide a broad range of otherapplications and services that allow members the opportunity to shareand receive information, often customized to the interests of themember. For example, with some embodiments, the social network servicemay include a photo sharing application that allows members to uploadand share photos with other members. With some embodiments, members maybe able to self-organize into groups, or interest groups, organizedaround a subject matter or topic of interest. With some embodiments, thesocial network service may host various job listings providing detailsof job openings with various organizations.

As members interact with the various applications, services and contentmade available via the social network service, the members' behavior(e.g., content viewed, links or member-interest buttons selected, etc.)may be monitored and information concerning the member's activities andbehavior may be stored, for example, as indicated in FIG. 1 by thedatabase with reference number 32.

With some embodiments, the social network system 20 includes what isgenerally referred to herein as an identity inference system 200. Theidentity inference system 200 is described in more detail below inconjunction with FIG. 2.

Although not shown, with some embodiments, the social network system 20provides an application programming interface (API) module via whichthird-party applications can access various services and data providedby the social network service. For example, using an API, a third-partyapplication may provide a user interface and logic that enables anauthorized representative of an organization to publish messages from athird-party application to a content hosting platform of the socialnetwork service that facilitates presentation of activity or contentstreams maintained and presented by the social network service. Suchthird-party applications may be browser-based applications, or may beoperating system-specific. In particular, some third-party applicationsmay reside and execute on one or more mobile devices (e.g., phone, ortablet computing devices) having a mobile operating system.

Turning now to FIG. 2, an identity inference system 200 includes adetermination module 202, a request generation module 204, and adatabase 206. The modules of the identity inference system 200 may beimplemented on or executed by a single device such as an inferredidentity device, or on separate devices interconnected via a network.The aforementioned inferred identity device may be, for example, one ormore client machines or application servers. The operation of each ofthe aforementioned modules of the identity inference system 200 will nowbe described in greater detail in conjunction with FIG. 3.

FIG. 3 is a flowchart illustrating an example method 300, according tovarious example embodiments. The method 300 may be performed at least inpart by, for example, the identity inference system 200 illustrated inFIG. 2 (or an apparatus having similar modules, such as one or moreclient machines or application servers). In operation 301 in FIG. 3, theidentification module 202 identifies a member profile attribute missingfrom a member profile page associated with a particular member of anonline social network service. In some embodiments, the missing memberprofile attribute corresponds to at least one of a geographic location,an educational attribute, an employer attribute, a title, and a skill.In operation 302 in FIG. 3, the prediction module 204 accesses memberprofile data associated with a plurality of members of the online socialnetwork service. The member profile data may be stored in the database206. In operation 303 in FIG. 3, the prediction module 204 accessesbehavioral log data associated with the members, the behavioral log dataindicating interactions by the members with one or more products of theonline social network service. The behavioral log data may be stored inthe database 206. In operation 304 in FIG. 3, the prediction module 204performs prediction modeling, based on a prediction model and featuredata including the member profile data and the behavioral log data, togenerate a confidence score associated with the particular member andthe missing member profile attribute, the confidence score indicating alikelihood that the missing member profile attribute corresponds to acandidate value. In some embodiments, the confidence score may be anumerical number within a predetermined range (e.g., a number within therange of 0 to 1, or a number within the range of 0 to 100, etc.). Insome embodiments, the prediction model is any one of a logisticregression model, a gradient-boosted machine (GBM) model, a Naïve Bayesmodel, a support vector machines (SVM) model, a decision trees model,and a neural network model.

FIG. 4 is a flowchart illustrating an example method 400, consistentwith various embodiments described above. In some embodiments, themethod 400 may be performed after the method 300 illustrated in FIG. 3.The method 400 may be performed at least in part by, for example, theidentity inference system 200 illustrated in FIG. 2 (or an apparatushaving similar modules, such one or more client machines or applicationservers). In operation 401, the prediction module 204 determines that aconfidence score (e.g., the confidence score generated in operation 304in FIG. 3) is greater than a predetermined threshold (e.g., 0.5, 0.8,etc.). In operation 402, the prediction module 204 prompts a particularmember associated with the confidence score to update a missing memberprofile attribute (e.g., the missing member profile attribute identifiedin operation 301 in FIG. 3) to correspond to a candidate valueassociated with the confidence score (e.g., see operation 304 in FIG.3). In some embodiments, the prompting may comprise displaying, via auser interface, a prompt that invites the particular member to updatethe missing member profile attribute to correspond to the candidatevalue. In some embodiments, the prompting may comprise transmitting amessage to the particular member that invites the particular member toupdate the missing member profile attribute to correspond to thecandidate value. For example, the aforementioned message may correspondto an email, a text message, a social network instant message, and achat message. It is contemplated that the operations of method 400 mayincorporate any of the other features disclosed herein. Variousoperations in the method 400 may be omitted or rearranged, as necessary.

In some embodiments, before prompting a member to update a missingmember profile attribute corresponding to a candidate value, the systemmay first determine whether or not that candidate value is alreadyincluded in the member's profile page (e.g., in correspondence withanother, existing member profile attribute). For example, suppose aparticular member already has the employer LinkedIn® listed on theirprofile as a former employer, but the current employer attribute ismissing. The identity inference system 200 may nevertheless infer thatthe member currently works at LinkedIn® (e.g., that the candidate valuefor the missing member profile attribute of current employer most likelycorresponds to the employer LinkedIn®). However, the fact that themember has already listed LinkedIn® as a former employer will result inthe system preventing the member from being prompted to update theircurrent employer to LinkedIn®. Accordingly, in some embodiments, thesystem may analyze the member's profile page for various values (e.g.,companies, universities, locations, titles, skills, etc.), and preventthose values from being sent as candidate values in the predictionmodeling process. Instead or in addition, after the prediction modelingprocess is completed, the system may ignore any confidence scoresassociated with candidate values already included in the member'sprofile page.

FIG. 5 is a flowchart illustrating an example method 500, consistentwith various embodiments described above. The method 500 may beperformed at least in part by, for example, the identity inferencesystem 200 illustrated in FIG. 2 (or an apparatus having similarmodules, such one or more client machines or application servers). Inoperation 501, the identification module 202 determines that a candidatevalue (e.g., output from a prediction modeling process) is associatedwith an existing member profile attribute included in a member profilepage of a member. For example, the existing member profile attribute maybe distinct from a missing member profile attribute in the memberprofile page (e.g., former employer vs. current employer). In operation502, the prediction module 204 prevents the particular member from beingprompted to update the missing member profile attribute to correspond tothe candidate value. It is contemplated that the operations of method500 may incorporate any of the other features disclosed herein. Variousoperations in the method 500 may be omitted or rearranged, as necessary.

In some embodiments, after the identity inference system 200 determinesthat a missing member profile attribute of the member corresponds to acandidate value (e.g., based on a confidence score being higher than apredetermined threshold, as described above), the system may modifyvarious types of information to indicate that the missing member profileattribute of the member likely corresponds to the candidate value. Forexample, if the system determines that a missing employer attributelikely corresponds to a member working at LinkedIn®, and if the systemreceives a request for a list of members that work at LinkedIn® (or asearch request for such members), then the system may include thatmember in the list of results (or indicate that the member should likelybe included in the results). As another example, if the identityinference system 200 is generating a count of current employees of aparticular employer such as LinkedIn® (e.g., to for the purposes ofmarketing and/or advertising), the system may increment this count toinclude the member.

FIG. 6 is a flowchart illustrating an example method 600, consistentwith various embodiments described above. The method 600 may beperformed at least in part by, for example, the identity inferencesystem 200 illustrated in FIG. 2 (or an apparatus having similarmodules, such one or more client machines or application servers). Inoperation 601, the prediction module 204 receives a search request formembers having a member profile attribute corresponding to a candidatevalue (e.g., search for members who work at LinkedIn®). In operation602, the prediction module 204 determines that a missing member profileattribute of a particular member likely corresponds to the candidatevalue, based on a confidence score in the associated with the particularmember and the candidate value. For example, the confidence score may becalculated in the operation 300 illustrated in FIG. 3 and may indicate alikelihood that a missing member profile attribute of the membercorresponds to the candidate value (e.g., the member may have a missingemployer attribute, and the confidence score associated with thecandidate value of the employer LinkedIn® is greater than apredetermined threshold, thereby indicating that the member likely worksat LinkedIn®). In operation 603, the prediction module 204 modifiessearch results associated with the search request that was received inoperation 601 to include in and/or identify the particular member ashaving the member profile attribute corresponding to the candidate value(e.g., the search results may indicate that the member works atLinkedIn® or likely works at LinkedIn®). It is contemplated that theoperations of method 600 may incorporate any of the other featuresdisclosed herein. Various operations in the method 600 may be omitted orrearranged, as necessary.

According to various example embodiments, the identity inference system200 may also provide various techniques for verifying or validatingexisting member profile attributes for a member. For example, theidentity inference system 200 may identify a member profile attributethat is already included on a member's profile page (e.g., the memberindicates that they work at Google®). The identity inference system 200may then perform the prediction modeling process in a similar manner asin embodiments described above (e.g., in connection with FIG. 3) inorder to generate various confidence score is associated with variouscandidate values for that member profile attribute. In this way, thesystem may determine, for example, that when the candidate value is setto the current value as specified by the user (e.g., LinkedIn®, and ifthe user-specified that they work at LinkedIn®), the generatedconfidence score is relatively quite low, indicating that the userlikely doesn't work at LinkedIn®. Similarly, the identity inferencesystem 200 may determine that, for example, the candidate valueassociated with some other employer (e.g., Apple®) is actuallyrelatively high, indicating that the user likely works at Apple®.Accordingly, the identity inference system 200 allows for verificationand validation of various member profile attributes included in amember's profile, such as geographic location, employer, University, totitle, skills, etc.

In conjunction with the verification techniques described above, if theidentity inference system 200 determines that an existing member profileattribute of the member is inaccurate (e.g., the member indicated thatthey work at Google®, when it seems likely that they work at Apple®),the identity inference system 200 may prompt the member, via web-baseduser interface, or via a mobile application, or a message (e.g., e-mail,text message, chat message, etc.), to update their member profileattribute accordingly. For example, the system may prompt the user tochange their employer from Google® to Apple®.

In some embodiments, in conjunction with the verification techniquesdescribed above, after the identity inference system 200 determines thatan existing member profile attribute of the member is likely notaccurate, the system may modify various types of information to indicatethat the missing member profile attribute of the member likelycorresponds to the more accurate candidate value. For example, if amember indicated that they work at Google®), and the system determinesthat it is more likely that they work at Apple®, and if the systemreceives a request for a list of members that work at Apple® (or asearch request for such members), then the system may include thatmember in the list of results (or indicate that the member should likelybe included in the results). Likewise, if the system receives a requestfor a list of members that work at Google® (or a search request for suchmembers), then the system may exclude that member in the list of results(or indicate that the member should likely not be included in theresults). As another example, if the identity inference system 200 isgenerating a count of current employees at Apple® (e.g., to for thepurposes of marketing and/or advertising), the system may increment thiscount to include the member, whereas the system may decrement the countfor the employer Google®.

FIG. 7 is a flowchart illustrating an example method 700, consistentwith various embodiments described above. The method 700 may beperformed at least in part by, for example, the identity inferencesystem 200 illustrated in FIG. 2 (or an apparatus having similarmodules, such one or more client machines or application servers). Inoperation 701, the identification module 202 identifies an existingmember profile attribute included in the member profile page of aparticular member. In operation 702, the prediction module 204 generatesa confidence score associated with the particular member and theexisting member profile attribute, a confidence score indicating thelikelihood that a new value distinct from a current value of theexisting member profile attribute is accurate. In operation 703, theprediction module 204 prompts the particular member to update theexisting member profile attribute to correspond to the new value, basedon the confidence score. It is contemplated that the operations ofmethod 700 may incorporate any of the other features disclosed herein.Various operations in the method 700 may be omitted or rearranged, asnecessary.

Example Prediction Models

As described above, the prediction module 204 may use any one of variousknown prediction modeling techniques to perform the prediction modeling.For example, according to various exemplary embodiments, the predictionmodule 204 may apply a statistics-based machine learning model such as alogistic regression model to the member profile data and/or behaviorallog data associated with one or more members of an online socialnetwork. As understood by those skilled in the art, logistic regressionis an example of a statistics-based machine learning technique that usesa logistic function. The logistic function is based on a variable,referred to as a logit. The logit is defined in terms of a set ofregression coefficients of corresponding independent predictorvariables. Logistic regression can be used to predict the probability ofoccurrence of an event given a set of independent/predictor variables. Ahighly simplified example machine learning model using logisticregression may be ln[p/(1−p)]=a+BX+e, or [p/(1−p)]=exp(a+BX+e), where lnis the natural logarithm, log_(exp), where exp=2.71828 . . . , p is theprobability that the event Y occurs, p(Y=1), p/(1−p) is the “oddsratio”, ln[p/(1−p)] is the log odds ratio, or “logit”, a is thecoefficient on the constant term, B is the regression coefficient(s) onthe independent/predictor variable(s), X is the independent/predictorvariable(s), and e is the error term. In some embodiments, theindependent/predictor variables of the logistic regression model maycorrespond to member profile data or behavioral log data associated withmembers of an online social network service (where the aforementionedmember profile data or behavioral log data may be encoded into numericalvalues and inserted into feature vectors). The regression coefficientsmay be estimated using maximum likelihood or learned through asupervised learning technique from the recruiting intent signature data,as described in more detail below. Accordingly, once the appropriateregression coefficients (e.g., B) are determined, the features includedin a feature vector (e.g., member profile data and/or behavioral logdata associated with one or more members of a social network service)may be plugged in to the logistic regression model in order to predictthe probability (or “confidence score”) that the event Y occurs (wherethe event Y may be, for example, a missing member profile attributecorresponding to a particular candidate value). In other words, provideda feature vector including various member profile data and/or behavioralfeatures associated with members, the feature vector may be applied to alogistic regression model to determine the probability that a missingmember profile attribute of a particular member corresponds to aparticular candidate value. Logistic regression is well understood bythose skilled in the art, and will not be described in further detailherein, in order to avoid occluding various aspects of this disclosure.The prediction module 304 may use various other prediction modelingtechniques understood by those skilled in the art to generate theaforementioned confidence score. For example, other prediction modelingtechniques may include other computer-based machine learning models suchas a gradient-boosted machine (GBM) model, a Naïve Bayes model, asupport vector machines (SVM) model, a decision trees model, and aneural network model, all of which are understood by those skilled inthe art.

According to various embodiments described above, the feature data maybe used for the purposes of both off-line training (for generating,training, and refining a prediction model and or the coefficients of aprediction model) and online inferences (for generating confidencescores). For example, if the prediction module 204 is utilizing alogistic regression model (as described above), then the regressioncoefficients of the logistic regression model may be learned through asupervised learning technique from the feature data. Accordingly, in oneembodiment, the identity inference system 200 may operate in an off-linetraining mode by assembling the feature data into feature vectors. Thefeature vectors may then be passed to the prediction module 204, inorder to refine regression coefficients for the logistic regressionmodel. For example, statistical learning based on the AlternatingDirection Method of Multipliers technique may be utilized for this task.Thereafter, once the regression coefficients are determined, theidentity inference system 200 may operate to perform online (or offline)inferences based on the trained model (including the trained modelcoefficients) on a feature vector representing the feature data of aparticular member of the online social network service. According tovarious exemplary embodiments, the off-line process of training theprediction model based on member profile data and behavioral log datamay be performed periodically at regular time intervals (e.g., once aday), or may be performed at irregular time intervals, random timeintervals, continuously, etc. Thus, since member profile data andbehavioral log data may change over time, it is understood that theprediction model itself may change over time (based on the currentmember profile data and behavioral log data used to train the model).

Example Mobile Device

FIG. 8 is a block diagram illustrating the mobile device 800, accordingto an example embodiment. The mobile device may correspond to, forexample, one or more client machines or application servers. One or moreof the modules of the system 200 illustrated in FIG. 2 may beimplemented on or executed by the mobile device 800. The mobile device800 may include a processor 810. The processor 810 may be any of avariety of different types of commercially available processors suitablefor mobile devices (for example, an XScale architecture microprocessor,a Microprocessor without Interlocked Pipeline Stages (MIPS) architectureprocessor, or another type of processor). A memory 820, such as a RandomAccess Memory (RAM), a Flash memory, or other type of memory, istypically accessible to the processor 810. The memory 820 may be adaptedto store an operating system (OS) 830, as well as application programs840, such as a mobile location enabled application that may providelocation based services to a user. The processor 810 may be coupled,either directly or via appropriate intermediary hardware, to a display850 and to one or more input/output (I/O) devices 860, such as a keypad,a touch panel sensor, a microphone, and the like. Similarly, in someembodiments, the processor 810 may be coupled to a transceiver 870 thatinterfaces with an antenna 890. The transceiver 870 may be configured toboth transmit and receive cellular network signals, wireless datasignals, or other types of signals via the antenna 890, depending on thenature of the mobile device 800. Further, in some configurations, a GPSreceiver 880 may also make use of the antenna 890 to receive GPSsignals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more processors may be configured by software (e.g.,an application or application portion) as a hardware-implemented modulethat operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses) thatconnect the hardware-implemented modules. In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or processors or processor-implementedmodules. The performance of certain of the operations may be distributedamong the one or more processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processor or processors may be located in a singlelocation (e.g., within a home environment, an office environment or as aserver farm), while in other embodiments the processors may bedistributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., Application Program Interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 9 is a block diagram of machine in the example form of a computersystem 900 within which instructions, for causing the machine to performany one or more of the methodologies discussed herein, may be executed.In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 900 includes a processor 902 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 904 and a static memory 906, which communicate witheach other via a bus 908. The computer system 900 may further include avideo display unit 910 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 900 also includes analphanumeric input device 912 (e.g., a keyboard or a touch-sensitivedisplay screen), a user interface (UI) navigation device 914 (e.g., amouse), a disk drive unit 916, a signal generation device 918 (e.g., aspeaker) and a network interface device 920.

Machine-Readable Medium

The disk drive unit 916 includes a machine-readable medium 922 on whichis stored one or more sets of instructions and data structures (e.g.,software) 924 embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 924 mayalso reside, completely or at least partially, within the main memory904 and/or within the processor 902 during execution thereof by thecomputer system 900, the main memory 904 and the processor 902 alsoconstituting machine-readable media.

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions or data structures. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention, or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 924 may further be transmitted or received over acommunications network 926 using a transmission medium. The instructions924 may be transmitted using the network interface device 920 and anyone of a number of well-known transfer protocols (e.g., HTTP). Examplesof communication networks include a local area network (“LAN”), a widearea network (“WAN”), the Internet, mobile telephone networks, Plain OldTelephone (POTS) networks, and wireless data networks (e.g., WiFi, LTE,and WiMAX networks). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine, and includes digitalor analog communications signals or other intangible media to facilitatecommunication of such software.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

1. A method comprising: identifying a member profile attribute missingfrom a member profile page associated with a particular member of anonline social network service; accessing member profile data associatedwith a plurality of members of the online social network service;accessing behavioral log data associated with the members, thebehavioral log data indicating interactions by the members with one ormore products of the online social network service; performingprediction modeling, using one or more processors, based on a predictionmodel and feature data including the member profile data and thebehavioral log data, to generate a confidence score associated with theparticular member and the missing member profile attribute, theconfidence score indicating a likelihood that the missing member profileattribute corresponds to a candidate value; determining that theconfidence score is greater than a predetermined threshold; receiving asearch request for members having a member profile attributecorresponding to the candidate value; modifying search resultsassociated with the search request to identify the particular memberbased on the determination; and prompting the particular member toupdate the missing member profile attribute to correspond to thecandidate value based on the determination.
 2. The method of claim 1,wherein the missing member profile attribute corresponds to at least oneof a geographic location, an educational attribute, an employerattribute, a title, and a skill.
 3. (canceled)
 4. The method of claim 1,wherein the missing member profile attribute corresponds to an employerattribute and the candidate value corresponds to a candidate employer.5. The method of claim 4, wherein the feature data in the model includesmember connection information identifying member connections of themembers of the online social network.
 6. The method of claim 4, whereinthe feature data in the model includes IP address informationidentifying IP addresses associated with login requests by the membersof the online social network.
 7. The method of claim 4, wherein thefeature data in the model includes profile view information identifyingprofile views by the members of the online social network.
 8. The methodof claim 4, wherein the feature data in the model includes email domaininformation identifying email domains associated with the members of theonline social network.
 9. The method of claim 4, wherein the featuredata in the model includes invitation information identifyinginvitations transmitted by the members of the online social network. 10.The method of claim 1, wherein the missing member profile attributecorresponds to an educational attribute and the candidate valuecorresponds to a candidate university.
 11. The method of claim 10,wherein the feature data in the model includes at least one of alumnigroup membership information, geographic location information, genderdistribution information, and industry information associated with themembers of the online social network.
 12. The method of claim 1, whereinthe missing member profile attribute corresponds to a geographiclocation and the candidate value corresponds to a candidate location.13. The method of claim 12, wherein the feature data in the modelincludes at least one of IP address information, member connectioninformation, and profile view information associated with the members ofthe online social network.
 14. The method of claim 1, wherein theprediction model is any one of a logistic regression model, agradient-boosted machine (GBM) model, a Naïve Bayes model, a supportvector machines (SVM) model, a decision trees model, and a neuralnetwork model.
 15. The method of claim 1, further comprising:determining that the missing member profile attribute of the particularmember corresponds to the candidate value, based on the confidencescore; and wherein the particular member is included in the modifiedsearch results.
 16. The method of claim 1, further comprising:identifying an existing member profile attribute included in the memberprofile page of the particular member; generating a second confidencescore associated with the particular member and the existing memberprofile attribute, the second confidence score indicating a likelihoodthat a new value distinct from a current value of the existing memberprofile attribute is accurate; and prompting the particular member toupdate the existing member profile attribute to correspond to the newvalue, based on the second confidence score.
 17. The method of claim 16,further comprising: revising a current count of employees at an employerassociated with the current value and an employer associated with thenew value, based on the second confidence score.
 18. The method of claim16, further comprising: receiving a search request for members having amember profile attribute corresponding to the current value; determiningthat the existing member profile attribute of the particular membercorresponds to the new value, based on the confidence score; andmodifying search results associated with the search request to excludethe particular member.
 19. A system comprising: a machine including amemory and at least one processor; an identification module, executableby the machine, configured to identify a member profile attributemissing from a member profile page associated with a particular memberof an online social network service; and a prediction module, executableby the machine, configured to: access member profile data associatedwith a plurality of members of the online social network service; accessbehavioral log data associated with the members, the behavioral log dataindicating interactions by the members with one or more products of theonline social network service; perform prediction modeling, using one ormore processors, based on a prediction model and feature data includingthe member profile data and the behavioral log data, to generate aconfidence score associated with the particular member and the missingmember profile attribute, the confidence score indicating a likelihoodthat the missing member profile attribute corresponds to a candidatevalue; determine that the confidence score is greater than apredetermined threshold; receive a search request for members having amember profile attribute corresponding to the candidate value; modifysearch results associated with the search request to identify theparticular member based on the determination; and prompt the particularmember to update the missing member profile attribute to correspond tothe candidate value based on the determination.
 20. A non-transitorymachine-readable storage medium comprising instructions that, whenexecuted by one or more processors of a machine, cause the machine toperform operations comprising: identifying a member profile attributemissing from a member profile page associated with a particular memberof an online social network service; accessing member profile dataassociated with a plurality of members of the online social networkservice; accessing behavioral log data associated with the members, thebehavioral log data indicating interactions by the members with one ormore products of the online social network service; performingprediction modeling, using one or more processors, based on a predictionmodel and feature data including the member profile data and thebehavioral log data, to generate a confidence score associated with theparticular member and the missing member profile attribute, theconfidence score indicating a likelihood that the missing member profileattribute corresponds to a candidate value; determining that theconfidence score is greater than a predetermined threshold; receiving asearch request for members having a member profile attributecorresponding to the candidate value; modifying search resultsassociated with the search request to identify the particular memberbased on the determination; and prompting the particular member toupdate the missing member profile attribute to correspond to thecandidate value based on the determination.
 21. A method comprising:identifying an existing member profile attribute included in a memberprofile page of a particular member; generating a confidence scoreassociated with the existing member profile attribute; determining thatthe existing member profile attribute is inaccurate based on thegenerated confidence score; receiving a search request for membershaving a member profile attribute corresponding to the existing memberprofile attribute; and modifying search results associated with thesearch request to exclude the particular member based on thedetermination.